The association of HLA-C and ERAP1 polymorphisms in early and late onset psoriasis and psoriatic arthritis patients of Hungary

Significant Correlation Between Cutaneous Abundance of Streptococcus and Psoriasis Severity in Patients with FBXL19 Gene Variants

Psoriasis results from both genetic predisposition and environmental triggers, such as Streptococcal infections. This study aimed to explore the correlation between the abundance of the Streptococcus genus on the skin and psoriasis severity in individuals carrying specific psoriasis-associated genetic variants. Studying 39 chronic plaque psoriasis patients, the elbow skin microbiome and 49 psoriasis-related single nucleotide polymorphisms (SNPs) were analysed using a MiSeq instrument for 16S rDNA sequencing, and CLC Genomic Workbench for processing and analysis. Through multivariate linear regression analysis, a positive correlation was found between Streptococcus genus abundance and psoriasis severity in patients with certain FBXL19 gene-related heterozygous SNPs (rs12924903, rs10782001, rs12445568). Conversely, a negative association was observed in patients with homozygous genotypes. Moreover, we identified an association between Streptococcus abundance and psoriasis severity in patients with genetic variants related to IL-22, ERAP1, NOS2, and ILF3. This is the first study highlighting a positive association between Streptococcus skin colonization and psoriasis severity in patients with heterozygous genotypes within the FBXL19 gene region. FXBL19 targets the IL-33/IL1RL1 axis, crucial in infectious diseases and innate immunity promotion. These novel results suggests an intricate interaction among host genetics, Streptococcus skin colonization, and psoriasis inflammation, offering potential avenues for novel treatment approaches.

A partitioned 88-loci psoriasis genetic risk score reveals HLA and non-HLA contributions to clinical phenotypes in a Newfoundland psoriasis cohort

Psoriasis is an immune-mediated inflammatory skin disease typically characterized by erythematous and scaly plaques. It affects 3% of the Newfoundland population while only affecting 1.7% of the general Canadian population. Recent genome-wide association studies (GWAS) in psoriasis have identified more than 63 genetic susceptibility loci that individually have modest effects. Prior studies have shown that a genetic risk score (GRS) combining multiple loci can improve psoriasis disease prediction. However, these prior GRS studies have not fully explored the association of GRS with patient clinical characteristics. In this study, we calculated three types of GRS: one using all known GWAS SNPs (GRS-ALL), one using a subset of SNPs from the HLA region (GRS-HLA), and the last using non-HLA SNPs (GRS-noHLA). We examined the relationship between these GRS and a number of psoriasis features within a well characterized Newfoundland psoriasis cohort. We found that both GRS-ALL and GRS-HLA were significantly associated with early age of psoriasis onset, psoriasis severity, first presentation of psoriasis at the elbow or knee, and the total number of body locations affected, while only GRS-ALL was associated with a positive family history of psoriasis. GRS-noHLA was uniquely associated with genital psoriasis. These findings clarify the relationship of the HLA and non-HLA components of GRS with important clinical features of psoriasis.

PSORS1 Locus Genotyping Profile in Psoriasis: A Pilot Case-Control Study

(1) Background: The psoriasis susceptibility 1 (PSORS1) locus, located within the major histocompatibility complex, is one of the main genetic determinants for psoriasis, the genotyping profile for three single-nucleotide polymorphisms (SNPs) comprising the PSORS1 locus: rs1062470 within PSORS1C1/CDSN genes, rs887466 within PSORS1C3 gene, rs10484554 within LOC105375015 gene, were investigated and correlated with psoriasis risk and severity. (2) Methods: This pilot case-controlled study involved 100 psoriatic patients and 100 healthy individuals. We investigated three SNPs and assessed the relative gene expression profile for the PSORS1C1 gene. We then correlated the results with both disease risk and severity. (3) Results: The most significantly associated SNP in PSORS1 locus with psoriasis was rs10484554 with its C/T genotype 5.63 times more likely to develop psoriasis under codominant comparison. Furthermore, C/T and T/T genotypes were 5 times more likely to develop psoriasis. The T allele was 3 times more likely to develop psoriasis under allelic comparison. The relative gene expression of PSORS1C1 for psoriatic patients showed to be under-expressed compared to normal controls. (4) Conclusions: Our study revealed the association of the three studied SNPs with psoriasis risk and severity in an Egyptian cohort, indicating that rs10484554 could be the major key player in the PSORS1 locus.

Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies

Unexplained genetic variation that causes complex diseases is often induced by gene-gene interactions (GGIs). Gene-based methods are one of the current statistical methodologies for discovering GGIs in case-control genome-wide association studies that are not only powerful statistically, but also interpretable biologically. However, most approaches include assumptions about the form of GGIs, which results in poor statistical performance. As a result, we propose gene-based testing based on the maximal neighborhood coefficient (MNC) called gene-based gene-gene interaction through a maximal neighborhood coefficient (GBMNC). MNC is a metric for capturing a wide range of relationships between two random vectors with arbitrary, but not necessarily equal, dimensions. We established a statistic that leverages the difference in MNC in case and in control samples as an indication of the existence of GGIs, based on the assumption that the joint distribution of two genes in cases and controls should not be substantially different if there is no interaction between them. We then used a permutation-based statistical test to evaluate this statistic and calculate a statistical p-value to represent the significance of the interaction. Experimental results using both simulation and real data showed that our approach outperformed earlier methods for detecting GGIs.